High Spatial Resolution Panchromatic Research Articles

Assessment of pan-sharpened very high-resolution WorldView-2 images

The fusion of multispectral (MS) images with high spatial resolution panchromatic (pan) images compensates the trade-off between spatial and spectral resolutions. The performance of fusion algorithms for different sensors is an active area of research. Therefore, with the availability of new very high-resolution (VHR) sensors, it becomes customary to evaluate the applicability of existing fusion techniques. In this study, we focused on the WorldView-2 (WV2) spaceborne sensor, which captures data in eight MS (2 m spatial resolution) bands and one pan (0.5 m spatial resolution) band. We compared and assessed 12 fusion techniques, namely Brovey transform (BT), Ehlers, Gram–Schimdt (GS), hyperspherical colour sphere (HCS), high-pass filter (HPF), modified intensity hue saturation (ModIHS), Multiplicative, PANSHARP, PANSHARP2, principal component (PC), and wavelet (IHS and PC)-based methods. To measure the quality of fused products, qualitative and quantitative methods were used. In qualitative methods, visual analysis of different colour composites was carried out. In quantitative methods, band-wise eight quality metrics including the required processing time and controlling parameters were reported. For overall image quality assessment, it is necessary to combine the values of different quality metrics. Therefore, a mean observation score based on these values was calculated to rank the fusion methods. It was observed that the HPF and PANSHARP methods produced the most visually appealing images, whereas quantitative values indicated that HCS, HPF, and PANSHARP methods performed better than other methods. Combining the results of quantitative and qualitative analyses, it was found that PANSHARP and HPF methods are superior to other methods in preserving both spatial and spectral details.

A comparative study of various pixel-based image fusion techniques as applied to an urban environment

In this paper, the use of high-resolution images for identification of urban features through pixel-based image fusion techniques is discussed. Fusion techniques are used to merge high spatial resolution panchromatic (PAN) image with low spatial resolution multispectral (MS) image to enhance the visual quality/appearance of some of the urban features present in the image. In this paper nine pixel-based fusion techniques, viz., Principal Component Analysis (PCA), multiplicative, Brovey transformation, wavelet analysis, subtractive, HPF, modified IHS, Ehlers, and hyperspherical color space, are discussed for the fusion of PAN and MS IKONOS images. The fused images are interpreted on the basis of visual comparison, correlation coefficients and histogram statistics. All the nine fusion techniques improved the resolution and the visual appearance of the original MS image. These are comparable to the resolution of the original PAN image. All the three resampling methods (nearest neighborhood, bilinear interpolation and the cubic convolution) do not have any significant effect on the final visual appearance of the fused images. All fusion techniques result in a change of the statistical parameters of the original images. The multiplicative technique results in major changes of the statistical parameters than the other techniques. After doing visual comparison and analyzing the statistical parameters, the Brovey and the PCA techniques seem to gather more information of urban features in fused PAN and MS IKONOS images.

A Sparse Image Fusion Algorithm With Application to Pan-Sharpening

Data provided by most optical Earth observation satellites such as IKONOS, QuickBird, and GeoEye are composed of a panchromatic channel of high spatial resolution (HR) and several multispectral channels at a lower spatial resolution (LR). The fusion of an HR panchromatic and the corresponding LR spectral channels is called “pan-sharpening.” It aims at obtaining an HR multispectral image. In this paper, we propose a new pan-sharpening method named Sparse F usion of Images (SparseFI, pronounced as “sparsify”). SparseFI is based on the compressive sensing theory and explores the sparse representation of HR/LR multispectral image patches in the dictionary pairs cotrained from the panchromatic image and its downsampled LR version. Compared with conventional methods, it “learns” from, i.e., adapts itself to, the data and has generally better performance than existing methods. Due to the fact that the SparseFI method does not assume any spectral composition model of the panchromatic image and due to the super-resolution capability and robustness of sparse signal reconstruction algorithms, it gives higher spatial resolution and, in most cases, less spectral distortion compared with the conventional methods.

Remote Sensing of Ecological Hotspots: Producing Value-added Information from Multiple Data Sources

Fusing high-spatial resolution panchromatic and high-spectral resolution multispectral images with complementary characteristics provides basis for complex land-use and land-cover type classifications. In this research, we investigated how well different pan sharpening algorithms perform when applied to single-sensor single-date and multi-senor multi–date images that encompass the Horton Plains national park (HPNP), a highly fragile eco-region that has been experiencing severe canopy depletion since 1970s, in Sri Lanka. Our aim was to deliver resolution-enhanced multitemporal images from multiple earth observation (EO) data sources in support of long-term dieback monitoring in the HPNP. We selected six candidate fusion algorithms: Brovey transform, Ehlers fusion algorithm, high-pass filter (HPF) fusion algorithm, modified intensity-hue-saturation (MIHS) fusion algorithm, principal component analysis (PCA) fusion algorithm, and the wavelet-PCA fusion algorithm. These algorithms were applied to eight different aerial and satellite images taken over the HPNP during last five decades. Fused images were assessed for spectral and spatial fidelity using fifteen quantitative quality indicators and visual inspection methods. Spectral quality metrics include correlation coefficient, root-mean-square-error (RMSE), relative difference to mean, relative difference to standard deviation, spectral discrepancy, deviation index, peak signal-to-noise ratio index, entropy, mean structural similarity index, spectral angle mapper, and relative dimensionless global error in synthesis. The spatial integrity of fused images was assessed using Canny edge correspondence, high-pass correlation coefficient, RMSE of Sobel-filtered edge images, and Fast Fourier Transform correlation. The Wavelet-PCA algorithm exhibited the worst spatial improvement while the Ehlers.MIHS and PCA fusion algorithms showed mediocre results. With respect to our multidimensional quality assessment,the HPF emerged as the best performing algorithm for single-sensor single-date and multi-sensor multi-date data fusion.We further examined the effect of fusion in the object-based image analysis framework. Our subjective analysis showed the improvement of image object candidates when panchromatic images’ high-frequency information is injected to low resolution multispectral images.

Adjustable Model-Based Fusion Method for Multispectral and Panchromatic Images

In this paper, an adjustable model-based image fusion method for multispectral (MS) and panchromatic (PAN) images is developed. The relationships of the desired high spatial resolution (HR) MS images to the observed low-spatial-resolution MS images and HR PAN image are formulated with image observation models. The maximum a posteriori framework is employed to describe the inverse problem of image fusion. By choosing particular probability density functions, the fused HR MS images are solved using a gradient descent algorithm. In particular, two functions are defined to adaptively determine most regularization parameters using the partially fused results at each iteration, retaining one parameter to adjust the tradeoff between the enhancement of spatial information and the maintenance of spectral information. The proposed method has been tested using QuickBird and IKONOS images and compared to several known fusion methods using quantitative evaluation indices. The experimental results verify the efficacy of this method.

Vegetation extraction from IKONOS imagery using high spatial resolution index

In vegetation change monitoring and urban planning, the measurement and mapping of the green vegetation over the Earth play an important role. The normalized difference vegetation index (NDVI) is the most popular measure to generate vegetation maps which resolution depends on that of the input images. High-resolution imagery can lead to better vegetation classification accuracy. Various methods are proposed to provide high spatial resolution vegetation indices based on a fusion concept. IKONOS produces high spatial resolution panchromatic (Pan) images and moderate spatial resolution multispectral (MS) images. Generally, for an image fusion purpose, the conventional bi-cubic interpolation scheme is used to resize MS images. Nevertheless, this scheme fails around edges and consequently produces blurred edges and annoying artifacts in interpolated MS images. To avoid this problem, an artifact-free image interpolation method is proposed. This study presents a modified NDVI that provides high spatial resolution maps which differentiate vegetated surfaces from other surfaces when using IKONOS imagery. This vegetation index (HRNDVI: high resolution NDVI) is based on a newly derived formula including high spatial resolution information from IKONOS. The HRNDVI is computed based on the resampled MS images and the Pan images. The proposed vegetation index takes advantage of both the high spatial resolution information of Pan images and the robustness of the interpolation technique. Visual and quantitative analysis demonstrates that this index appears promising and performs well in vegetation extraction and visualization.

ISVR: an improved synthetic variable ratio method for image fusion

An Improved Synthetic Variable Ratio (ISVR) fusion method is proposed to merge high spatial resolution panchromatic (Pan) images and multispectral (MS) images based on a simulation of the panchromatic image from the multispectral bands. Compared to the existing SVR (Synthetic Variable Ratio) family methods, the ISVR method manifests two major improvements: a simplified and physically meaningful scheme to derive the parameters necessary as required by SVR, and less computing power. Two sets of IKONOS Pan and MS images: one in urban area and another one in a forest area, were used to evaluate the effectiveness of classification-oriented ISVR method in comparison to the Principal Component Substitution (PCS), Synthetic Variable Ratio (SVR) and Gram-Schmidt Spectral Sharpening (GS) methods that are available in the ENVI software package. Results indicate the ISVR method achieves the best spectral fidelity to facilitate classification compared to PCS, SVR, and GS methods.

A Model-Based Approach to Multiresolution Fusion in Remotely Sensed Images

In this paper, a model-based approach to multiresolution fusion of remotely sensed images is presented. Given a high spatial resolution panchromatic (Pan) image and a lowspatial resolution multispectral (MS) image acquired on the same geographical area, the presented method aims to enhance the spatial resolution of the MS image to the resolution of the Pan observation. The proposed fusion technique utilizes the spatial correlation of each of the high-resolution MS channels by using an autoregressive (AR) model, whose parameters are learnt from the analysis of the Pan data. Under the assumption that the parameters of the AR model for the Pan image are the same as those that represent the MS images due to spectral correlation, the proposed technique exploits the learnt parameter values in the context of a proper regularization technique to estimate the high spatial resolution fields for the MS bands. This results in a combination of the spectral characteristics of the low-resolution MS data with the high spatial resolution of the Pan image. The main advantages of the proposed technique are: 1) unlike standard methods proposed in the literature, it requires no registration between the Pan and the MS images; 2) it models effectively the texture of the scene during the fusion process; 3) it shows very small spectral distortion (as it is less affected, compared to standard methods, by the specific digital numbers of pixels in the Pan image, since it exploits the learnt parameters from the Pan image rather than the actual Pan digital numbers for fusion); and 4) it can be used in critical situations in which the Pan and the MS images are acquired (also by different sensors) in slightly different areas. Quantitative experimental results obtained using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Quickbird images point out the effectiveness of the proposed method

Selective Synthetic Aperture Radar and Panchromatic Image Fusion by Using the à Trous Wavelet Decomposition

Synthetic aperture radar (SAR) imaging sensor presents an important advantage for the earth change observation independently of weather conditions. However, the SAR image provides an incomplete information (as roads) of the observed scene leading thus to an ambiguous interpretation. In order to compensate the lack of features, the high spatial resolution panchromatic (P) image is often used as a complementary data for improving the quality of the SAR image. The concept is based on the extraction of features (details) from the P image in order to incorporate into the SAR image. Therefore, we propose an approach based on the use of the à trous wavelet decomposition (ATWD) for extracting features from the P image. Experimental results show that the SAR- composite image allows a better detection of lines, edges, and field boundaries.